Adversarial AI Attacks, Mitigations, and Defense Strategies: A cybersecurity professional’s guide to AI attacks, threat modeling, and securing AI with MLSecOps

“The book not only explains how adversarial attacks work but also shows you how to build your own test environment and run attacks to see how they can corrupt ML models. It’s a comprehensive guide that walks you through the technical details and then flips to show you how to defend against these very same attacks.”

– Elaine Doyle, VP and Cybersecurity Architect, Salesforce

Key FeaturesUnderstand the unique security challenges presented by predictive and generative AIExplore common adversarial attack strategies as well as emerging threats such as prompt injectionMitigate the risks of attack on your AI system with threat modeling and secure-by-design methodsPurchase of the print or Kindle book includes a free PDF eBookBook Description

Adversarial attacks trick AI systems with malicious data, creating new security risks by exploiting how AI learns. This challenges cybersecurity as it forces us to defend against a whole new kind of threat. This book demystifies adversarial attacks and equips you with the skills to secure AI technologies, moving beyond research hype or business-as-usual activities. Learn how to defend AI and LLM systems against manipulation and intrusion through adversarial attacks such as poisoning, trojan horses, and model extraction, leveraging DevSecOps, MLOps, and other methods to secure systems.

This strategy-based book is a comprehensive guide to AI security, combining structured frameworks with practical examples to help you identify and counter adversarial attacks. Part 1 introduces the foundations of AI and adversarial attacks. Parts 2, 3, and 4 cover key attack types, showing how each is performed and how to defend against them. Part 5 presents secure-by-design AI strategies, including threat modeling, MLSecOps, and guidance aligned with OWASP and NIST. The book concludes with a blueprint for maturing enterprise AI security based on NIST pillars, addressing ethics and safety under Trustworthy AI.

By the end of this book, you’ll be able to develop, deploy, and secure AI systems against the threat of adversarial attacks effectively.

What you will learnSet up a playground to explore how adversarial attacks workDiscover how AI models can be poisoned and what you can do to prevent thisLearn about the use of trojan horses to tamper with and reprogram modelsUnderstand supply chain risksExamine how your models or data can be stolen in privacy attacksSee how GANs are weaponized for Deepfake creation and cyberattacksExplore emerging LLM-specific attacks, such as prompt injectionLeverage DevSecOps, MLOps and MLSecOps to secure your AI systemWho this book is for

This book tackles AI security from both angles – offense and defence. AI developers and engineers will learn how to create secure systems, while cybersecurity professionals, such as security architects, analysts, engineers, ethical hackers, penetration testers, and incident responders will discover methods to combat threats to AI and mitigate the risks posed by attackers. The book also provides a secure-by-design approach for leaders to build AI with security in mind.

To get the most out of this book, you’ll need a basic understanding of security, ML concepts, and Python.

Table of ContentsGetting Started with AIBuilding Our Adversarial PlaygroundSecurity and Adversarial AIPoisoning AttacksModel Tampering with Trojan Horses and Model ReprogrammingSupply Chain Attacks and Adversarial AIEvasion Attacks against Deployed AIPrivacy Attacks – Stealing Models

(N.B. Please use the Read Sample option to see further chapters)

From the Publisher

Why learn about adversarial AI attacks?​

AI is a new revolution in the making, transforming our lives every day. Alongside the phenomenal opportunities presented by AI, new risks and threats are emerging, especially in the area of cybersecurity, and new skills are demanded to safeguard AI systems.​

Some of the threats to AI manipulate the very essence of how AI works to trick these systems. We call this adversarial AI. Understanding adversarial AI attacks and defending against them is challenging because it requires an understanding of AI and Machine Learning (ML) techniques.

With an understanding of how adversarial AI attacks work, you’ll be well-positioned to defend and mitigate the risks of adversarial attacks on your AI systems.​

What will this book do for me?​

With this book, you’ll build a foundational hands-on understanding of AI and ML and the advanced security challenges they present. You’ll develop this through practical walkthroughs of adversarial AI techniques, examples, and countermeasures, exploring attacks from both offensive and defensive perspectives.

In this book you will act as an attacker, staging attacks to demonstrate the threats they present, and then learn how to mitigate them with actionable advice and tips. You’ll also explore a methodology for secure-by-design AI with core elements such as threat modeling and MLSecOps, while looking at trustworthy AI. The knowledge and practical skills you will gain are essential to safeguard AI against its abusers in these rapidly changing times.

Key types of adversarial AI attacks you’ll learn how to protect your AI against in this book:​

Model development attacks​

Model development attacks we explore include:​

Poisoning attacks​Trojan horse attacks​Adversarial AI attacks through the supply chain

Attacks on deployed AI

Attacks on deployed AI we explore include:​

Evasion attacks​Attacks to steal models​Attacks to steal data​

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Attacks on generative AI ​

Attacks on generative AI we explore include:​

The weaponization of GANs​Prompt injection attacks​Poisoning of embeddings in RAG and in the fine-tuning of LLMs​Model inversion attacks​

Publisher ‏ : ‎ Packt Publishing
Publication date ‏ : ‎ July 26, 2024
Language ‏ : ‎ English
Print length ‏ : ‎ 586 pages
ISBN-10 ‏ : ‎ 1835087981
ISBN-13 ‏ : ‎ 978-1835087985
Item Weight ‏ : ‎ 2.22 pounds
Dimensions ‏ : ‎ 1.52 x 7.5 x 9.25 inches